Behavioral changes associated with ambulation activity among older adults with dementia may indicate underlying physiological vulnerabilities. Using a real-time locating system (RTLS), work by the author shows intra-individual changes in ambulation activity (e.g., continuous ambulation) are meaningful changes in behaviour associated with fall vulnerability in a population of institutionalized older adults with dementia and may indicate the onset of acute medical conditions. Other research using RTLS technology in a similar sample suggests increased tortuosity (e.g., random changes in direction during movement) and/or the onset of dementia-related wandering behaviours (lapping and pacing ambulation patterns) may be linked to a fall and other acute changes in physical health. Thus, changes in the quantity (e.g., increased time and distance travelled) and/or quality (e.g., random changes in direction, lapping and pacing patterns) of ambulation activity may increase fall vulnerability as these older adults are ambulating more or in a more tortuous path or may be an early sign of an acute medical condition that the person cannot communicate. Specific Aim 1, Study 1: With a larger more heterogeneous sample, test a new model of ambulation activity. H1a: Intra-individual changes in ambulation activity (e.g., path characteristics, tortuosity) will be significantly associated with a fall H1b: and the onset of acute medical conditions. Specific Aim 2, Study 2: Enhance the predictive capability of the model to capture patterns of changes in ambulation activity associated with acute changes in physical health. RQ: How will using innovative predictive analytics such as random forest (RF), partial least squares (PLS) and linear classifiers to identify additional predictive factors and ambulation patterns (e.g., lapping, pacing) improve the predictive capability of the model? Specific Aim 3, Study 3: Translate model findings in to clinical strategies that may be used to modify individual plans of care. Rationale: Findings from machine-learning approaches may need additional interpretation to be clinically feasible; identifying underlying mechanisms through collaboration with experts in a descriptive pilot and translating these into practice with focus groups of VA CLC clinicians will lead to strategies that may be used in VA CLC's to improve patient care. Study Design: A prospective longitudinal natural history study design will be used in Study 1. In Study 2, a predictive modeling design will be used to identify additional factors that improve the predictive capability of the model. In study 3, a qualitative study design will be used. Methodology: In Study 1, ambulation activity including time and distance travelled, gait speed, path characteristics (e.g., continuous walking), ambulation patterns (lapping and pacing) and tortuosity (random changes in direction and movement) will be measured continuously by a RTLS and associated with falls and acute medical conditions for up to 2 years. Study 2 will use historical data from Aim 1, machine learning and other statistical techniques including random forest (RF), partial least squares (PLS) and linear classifiers to examine additional factors and combinations of factors associated with falls and acute medical conditions. In study 3, with experts, clinical staff focus groups will be used to identify and explore clinical strategies that may reduce fall risk and the early identification of acute medical conditions Impact: This work will lead to a better understanding of behavioral changes associated with ambulation activity and how these changes may lead to an increased risk for acute changes in physical health.